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CIELAB is commonly used in design as it provides a simple method for approximating color difference. However, these approximations model color perception under laboratory conditions, with correctly calibrated displays and carefully constrained viewing environments that are not reflective of complexity of viewing conditions encountered in the real world. In this paper, we present a data-driven engineering model for parametric color difference that extends CIELAB to be more broadly applicable to real-world conditions. Our model can be tuned to a desired range of viewers and conditions using a simple modeling procedure, while minimally increasing the complexity of the model. We demonstrate our approach empirically by modeling color differences for the web by leveraging crowdsourced participants.